提出一种新型随机森林方法，用于右删失数据的非线性分位数预测。
Global Censored Quantile Random Forest
- 基于随机森林构建分位数预测模型，无需线性假设。
- 在模拟和真实数据上均优于现有方法，预测精度显著提升。
- 提供基于预测准确性的特征重要性排序，适合生存分析场景。
近年来，删失分位数回归在生存分析中日益流行，但多数方法依赖线性假设。本文提出全局删失分位数随机森林（GCQRF），用于在右删失数据上预测条件分位数过程。该方法基于随机森林，具有灵活性与竞争力，能捕捉复杂非线性关系。通过考虑树结构中的随机性，并将方法与随机不完备无限阶U过程（IDUP）关联，我们在不假设无限森林的前提下量化了预测过程的方差，并建立了其弱收敛性。此外，提出了基于样本外预测准确性的特征重要性度量。实验表明，该方法在多种数据集上均优于现有方法，且在模拟与真实数据上验证了重要性度量的有效性。
原文摘要 · Abstract (English)
In recent years, censored quantile regression has enjoyed an increasing popularity for survival analysis while many existing works rely on linearity assumptions. In this work, we propose a Global Censored Quantile Random Forest (GCQRF) for predicting a conditional quantile process on data subject to right censoring, a forest-based flexible, competitive method able to capture complex nonlinear relationships. Taking into account the randomness in trees and connecting the proposed method to a randomized incomplete infinite degree U-process (IDUP), we quantify the prediction process' variation without assuming an infinite forest and establish its weak convergence. Moreover, feature importance ranking measures based on out-of-sample predictive accuracy are proposed. We demonstrate the superior predictive accuracy of the proposed method over a number of existing alternatives and illustrate the use of the proposed importance ranking measures on both simulated and real data.
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